The OHDSI approach to RWD and RWE
The Observational Health Data Sciences and Informatics (OHDSI) is a global initiative launched in 2014 to address the lack of uniformity in collecting and managing observational health data. OHDSI’s primary goal is to promote scientific research by fostering the use of such data. The OHDSI community is dedicated to creating and implementing a harmonized data model and standardized vocabularies, along with developing tools and methodologies for aligning and analyzing Real World Data (RWD).
By using the OHDSI methodology to harmonize data collection and analysis, researchers can generate reliable Real World Evidence (RWE). This enables researchers to evaluate the safety and efficacy of medical interventions and monitor disease progression effectively. The OHDSI approach empowers healthcare professionals to make informed decisions about patient care and enhance the quality of healthcare services. Overall, the OHDSI initiative represents a significant advancement in leveraging observational health data for scientific research, leading to improved health outcomes for patients.
If you are looking to start or advance your OHDSI Journey, contact The Hyve today to learn more about our OHDSI tools and OMOP CDM adoption roadmap development services. We can help you navigate the complexity of the OHDSI ecosystem and tailor a project collaboration to suit your specific needs.
The three stages of the OHDSI journey & their value propositions
At The Hyve, our RWD Team has developed a three-stage process called the OHDSI Journey to support clients in adopting OHDSI tooling and the OMOP CDM. This process results in the establishment of a repeatable in-house process for generating actionable RWE on demand.
Let’s explore the three OHDSI Journey stages and their value propositions in more detail.
Stage 1: OMOP CDM / OHDSI approach awareness level
Shaping RWD/RWE use cases, establishing the value of adopting OMOP CDM
In the first stage, client teams familiarize themselves with the OMOP CDM and OHDSI tools through consulting services and hands-on training and workshops. With the help of The Hyve team, clients identify potential RWD use cases and evaluate the benefits of incorporating the OMOP CDM and OHDSI analytical tools into their operations. This stage establishes the groundwork for the next stage, which focuses on harmonizing RWD source datasets to the OMOP CDM.
Stage 2: In-house source RWD not yet harmonized
Harmonizing source data to OMOP CDM, high-quality analysis-ready OMOP databases
In the second stage, client teams transform their source data into the OMOP CDM with the help of The Hyve’s consultants and technical data engineering experts. This stage concentrates on the conversion of variable RWD source datasets into OMOP format, ensuring that the final OMOPed data is reliable, accurate, fit-for-purpose and analysis-ready.
Stage 3: RWD datasets already harmonized to OMOP CDM
Using RWD harmonized in OMOP CDM to generate actionable RWE insights
In the third stage, client in-house RWD that has already been harmonized to OMOP CDM is analyzed using open-source OHDSI or in-house analytical tools. The primary objective of this stage is to generate valuable RWE insights. Another key objective is to establish an actionable RWE feedback loop - in which answering certain RWE questions opens the need for repeating aspects of stage 1 (new use cases) and stage 2 (new source data being harmonized to OMOP CDM).
Project Types per OHDSI Journey Stage
Considering the OHDSI Journey stages and their corresponding key value propositions, how does The Hyve RWD team approach helping client teams achieve their RWE goals?
Our team offers several types of projects, grouped across the three OHDSI Journey stages:
Stage 1: Introductory Modular OHDSI Workshops
The OHDSI community provides a robust set of tools for leveraging RWD to inform healthcare decisions. However, understanding and working with these tools and observational data workflows can be challenging, especially for beginners. This is where The Hyve's OHDSI/OMOP workshops serve as a useful start. The Hyve offers introductory training that is customized for various participant groups, such as general audiences, data engineers, data analysts, or a combination thereof.
The Hyve's workshops are designed in a modular format, allowing participants to select relevant modules based on their needs. The workshops provide a comprehensive understanding of OMOP CDM and OHDSI tools, covering areas such as source data profiling, data quality verification, and cohort building. Overall, The Hyve’s OHDSI/OMOP workshops are a valuable resource for client teams who are embarking on their OHDSI Journey and seeking to gain an in-depth understanding of these powerful tools.
Stage 2: Full OHDSI tools suite deployment + SLA
The Hyve team is committed to delivering secure and dependable solutions to assist client teams in harmonizing diverse RWD source datasets into the OMOP CDM. Our approach involves deploying the full suite of OHDSI tools and providing a Service Level Agreement (SLA) to ensure that technical setup details are taken care of while client teams focus on gaining valuable RWE insights.
Deploying all OHDSI tools together as a full suite reduces the risk of compatibility issues and ensures seamless integration and accompanying workflows. An OHDSI tool SLA, such as the one offered by Hyve, guarantees uptime, performance, and support response times. This ensures a stable deployment and timely and efficient resolution of any troubleshooting issues.
Stage 2: Data harmonization into OMOP CDM - as a service (CDM aaS)
Integrating data from various sources, such as electronic health records, claims databases, and clinical trials, and performing cross-dataset analytics is feasible only after the source-format RWD datasets are harmonized in the OMOP CDM format. The Hyve’s data harmonization service offers a convenient and cost-effective solution for transforming source-format datasets into the OMOP CDM format.
With our end-to-end CDM as a Service (CDM aaS) offering, our team handles the intricate data conversion process, thereby freeing clients to focus on data analysis as opposed to data conversion management. The Hyve ETL development service is user-friendly and budget-conscious, enabling prompt initiation of data analysis in the OMOP CDM format.
Stage 2: Using an ETL framework for multiple ETLs development
The ETL (Extract, Transform, Load) process is a critical step in data warehousing, involving the extraction of data from diverse sources, its transformation into a harmonized format, and loading into the OMOP CDM target system. However, as there is no standardized ETL framework for converting source data to the OMOP CDM, the variable data conversion steps in different ETL development projects can lead to cost-inefficiency, repetitiveness, and a higher risk of data quality issues.
Adopting an ETL framework for developing multiple ETLs for various source datasets provides several key benefits, including:
streamlining data transformation into the OMOP CDM format with reusable tools and processes, saving time and effort in ETL development and maintenance
enhancing data quality with consistent rules for transforming and loading data into OMOP CDM format, reducing errors, and ensuring consistency across ETLs
simplifying the ETL process for managing large amounts of data from various sources.
At The Hyve, we utilize the Delphyne framework for the development of multiple ETL pipelines, enabling us to harmonize OMOP CDM data effectively and efficiently. This approach differentiates us from other vendors, ensuring the smooth and effective harmonization of multiple source datasets into the OMOP CDM format. By using Delphyne, essentially we harmonize the data harmonization process itself.
Stage 3: Data Quality Verification & ETL Optimization
Ensuring data quality is critical for accurate analysis and informed decision-making. Inaccurate or incomplete data can limit the research questions that can be addressed and lead to flawed conclusions, ultimately resulting in poor outcomes. At The Hyve, we provide OMOP CDM data quality verification and optimization services for existing ETLs to help organizations achieve optimal data quality.
Our OMOP data quality verification service involves a comprehensive evaluation of the data’s quality, utilizing various techniques to identify errors or inconsistencies that may affect the validity of the analysis. This ensures that the data is accurate, complete, and error-free.
Additionally, The Hyve optimizes existing ETL pipelines to improve data processing efficiency, minimizing the risk of errors and improving overall data quality. We identify and address bottlenecks or inefficiencies in the current processes, resulting in improved speed, accuracy, and reliability of the ETLs.
By utilizing The Hyve’s OMOP data quality verification and ETL optimization services, organizations can have confidence in the quality of their data, enabling them to make informed decisions and achieve better research and healthcare outcomes.
Stage 3: Data readiness for generating in-house RWE & study-a-thons participation
Finally, The Hyve provides data-readiness services to help generate in-house RWE and support participation in RWE study-a-thons with datasets from multiple data partners. Our team of experts assists in preparing data for both in-house RWE generation and RWE study-a-thons, ensuring appropriate formatting, structure, and validation. With extensive data management experience, we can help you overcome the common challenges in preparing OMOP data for analysis.
To facilitate in-house RWE generation at scale, we offer support in setting up the OHDSI analytics tool ATLAS. We also assist clients in developing and optimizing workflows used by OMOP data users in client organizations including data engineers and analysts.
Similarly, RWE study-a-thons are challenging analysis activities that require healthcare researchers to explore real-world data and address complex healthcare questions. Data preparation for such events can be time-consuming and difficult, involving tasks such as data cleaning, normalization, integration, and validation.
Utilizing The Hyve’s data readiness services ensures that clients have the necessary data to participate fully and effectively in RWE study-a-thons and generate RWE in-house. This enables clients to gain insights that can have a significant impact on healthcare decision-making, such as in the COVID-19 and PIONEER study-a-thons.
Summary of services & next steps
From source-format RWD to actionable RWE
The Hyve offers a comprehensive solution to unlocking the full potential of the life science organization's data. With our open-source technologies and expert consulting services, our RWD team will support you in maximizing the value of your data.
Join us in exploring the use of observational health data for RWE-driven healthcare research, and don’t miss out on this valuable opportunity to collaborate!
Roadmapping your OHDSI Journey
Are you feeling uncertain about how to proceed with OHDSI tools and the OMOP CDM? The Hyve is here to assist you.
As data management and analysis experts with extensive experience in the OHDSI framework, we can guide you through the process step by step. Our services include developing a tailored roadmap that outlines the next steps to take in your OHDSI journey, regardless of your current level of knowledge or use of the framework.
This roadmap takes into account the client team’s specific needs and objectives, as well as the current level of expertise and available resources. Whether you are just starting out or looking to expand your use of the framework, we will collaborate with you to create a clear and actionable plan for success.
What can you expect from The Hyve's OHDSI roadmap?
In short, it is a valuable approach for anyone looking to navigate the OHDSI framework successfully. With customized guidance, comprehensive coverage, expert insights, and clear direction, you can feel confident that you're on the right path toward achieving your RWE goals.
